Overview

Dataset statistics

Number of variables10
Number of observations2511
Missing cells31
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory196.3 KiB
Average record size in memory80.1 B

Variable types

Categorical1
Numeric8
Unsupported1

Alerts

Gemeinde has a high cardinality: 81 distinct valuesHigh cardinality
Gemeinde ID is highly overall correlated with GemeindeHigh correlation
Mittlere Wohnbevölkerung is highly overall correlated with Ständige Wohnbevölkerung Total and 2 other fieldsHigh correlation
Ständige Wohnbevölkerung Total is highly overall correlated with Mittlere Wohnbevölkerung and 2 other fieldsHigh correlation
Ständige Wohnbevölkerung Bevölkerungs-dichte1 in Pers./km2 is highly overall correlated with Mittlere Wohnbevölkerung and 3 other fieldsHigh correlation
Ständige Wohnbevölkerung Anteil 0-19-Jährige in % is highly overall correlated with Ständige Wohnbevölkerung Anteil 20-64-Jährige in % and 2 other fieldsHigh correlation
Ständige Wohnbevölkerung Anteil 20-64-Jährige in % is highly overall correlated with Ständige Wohnbevölkerung Bevölkerungs-dichte1 in Pers./km2 and 1 other fieldsHigh correlation
Ständige Wohnbevölkerung Anteil 65-Jährige und Ältere in % is highly overall correlated with Ständige Wohnbevölkerung Anteil 0-19-Jährige in %High correlation
Jahr is highly overall correlated with Ständige Wohnbevölkerung Anteil 0-19-Jährige in %High correlation
Gemeinde is highly overall correlated with Gemeinde ID and 3 other fieldsHigh correlation
Gemeinde ID has 31 (1.2%) missing valuesMissing
Gemeinde is uniformly distributedUniform
Ständige Wohnbevölkerung Ausländer-anteil in % is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-03-10 13:29:09.217385
Analysis finished2023-03-10 13:29:35.542079
Duration26.32 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Gemeinde
Categorical

HIGH CARDINALITY  HIGH CORRELATION  UNIFORM 

Distinct81
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
Kanton Luzern
 
31
Luthern
 
31
Root
 
31
Romoos
 
31
Römerswil
 
31
Other values (76)
2356 

Length

Max length19
Median length11
Mean length8.0987654
Min length4

Characters and Unicode

Total characters20336
Distinct characters46
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKanton Luzern
2nd rowAdligenswil
3rd rowAesch
4th rowAlberswil
5th rowAltbüron

Common Values

ValueCountFrequency (%)
Kanton Luzern 31
 
1.2%
Luthern 31
 
1.2%
Root 31
 
1.2%
Romoos 31
 
1.2%
Römerswil 31
 
1.2%
Roggliswil 31
 
1.2%
Rickenbach 31
 
1.2%
Reiden 31
 
1.2%
Rain 31
 
1.2%
Pfaffnau 31
 
1.2%
Other values (71) 2201
87.7%

Length

2023-03-10T14:29:35.889086image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
luzern 62
 
2.4%
ermensee 31
 
1.2%
alberswil 31
 
1.2%
altbüron 31
 
1.2%
altishofen 31
 
1.2%
ballwil 31
 
1.2%
beromünster 31
 
1.2%
buchrain 31
 
1.2%
büron 31
 
1.2%
buttisholz 31
 
1.2%
Other values (71) 2201
86.6%

Most occurring characters

ValueCountFrequency (%)
e 2201
 
10.8%
n 1705
 
8.4%
i 1705
 
8.4%
l 1302
 
6.4%
s 1147
 
5.6%
r 1116
 
5.5%
h 1054
 
5.2%
o 992
 
4.9%
a 899
 
4.4%
c 775
 
3.8%
Other values (36) 7440
36.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 17701
87.0%
Uppercase Letter 2573
 
12.7%
Dash Punctuation 31
 
0.2%
Space Separator 31
 
0.2%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 2201
12.4%
n 1705
 
9.6%
i 1705
 
9.6%
l 1302
 
7.4%
s 1147
 
6.5%
r 1116
 
6.3%
h 1054
 
6.0%
o 992
 
5.6%
a 899
 
5.1%
c 775
 
4.4%
Other values (13) 4805
27.1%
Uppercase Letter
ValueCountFrequency (%)
R 279
10.8%
E 279
10.8%
S 248
9.6%
H 248
9.6%
W 186
 
7.2%
M 186
 
7.2%
B 155
 
6.0%
A 155
 
6.0%
G 155
 
6.0%
D 93
 
3.6%
Other values (11) 589
22.9%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 20274
99.7%
Common 62
 
0.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 2201
 
10.9%
n 1705
 
8.4%
i 1705
 
8.4%
l 1302
 
6.4%
s 1147
 
5.7%
r 1116
 
5.5%
h 1054
 
5.2%
o 992
 
4.9%
a 899
 
4.4%
c 775
 
3.8%
Other values (34) 7378
36.4%
Common
ValueCountFrequency (%)
- 31
50.0%
31
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 20119
98.9%
None 217
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 2201
 
10.9%
n 1705
 
8.5%
i 1705
 
8.5%
l 1302
 
6.5%
s 1147
 
5.7%
r 1116
 
5.5%
h 1054
 
5.2%
o 992
 
4.9%
a 899
 
4.5%
c 775
 
3.9%
Other values (34) 7223
35.9%
None
ValueCountFrequency (%)
ü 155
71.4%
ö 62
 
28.6%

Gemeinde ID
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct80
Distinct (%)3.2%
Missing31
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean1077.975
Minimum1001
Maximum1151
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:36.270658image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile1006.9
Q11040.75
median1081.5
Q31121.25
95-th percentile1145.05
Maximum1151
Range150
Interquartile range (IQR)80.5

Descriptive statistics

Standard deviation44.023924
Coefficient of variation (CV)0.040839466
Kurtosis-1.1162486
Mean1077.975
Median Absolute Deviation (MAD)40.5
Skewness-0.019581716
Sum2673378
Variance1938.1059
MonotonicityNot monotonic
2023-03-10T14:29:36.688877image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1135 31
 
1.2%
1065 31
 
1.2%
1007 31
 
1.2%
1039 31
 
1.2%
1142 31
 
1.2%
1097 31
 
1.2%
1140 31
 
1.2%
1037 31
 
1.2%
1139 31
 
1.2%
1095 31
 
1.2%
Other values (70) 2170
86.4%
ValueCountFrequency (%)
1001 31
1.2%
1002 31
1.2%
1004 31
1.2%
1005 31
1.2%
1007 31
1.2%
1008 31
1.2%
1009 31
1.2%
1010 31
1.2%
1021 31
1.2%
1023 31
1.2%
ValueCountFrequency (%)
1151 31
1.2%
1150 31
1.2%
1147 31
1.2%
1146 31
1.2%
1145 31
1.2%
1143 31
1.2%
1142 31
1.2%
1140 31
1.2%
1139 31
1.2%
1137 31
1.2%
Distinct2037
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9083.1776
Minimum220
Maximum418337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:37.139759image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum220
5-th percentile667
Q11377.5
median2298
Q34487
95-th percentile14201.5
Maximum418337
Range418117
Interquartile range (IQR)3109.5

Descriptive statistics

Standard deviation41278.844
Coefficient of variation (CV)4.5445378
Kurtosis70.184397
Mean9083.1776
Median Absolute Deviation (MAD)1329
Skewness8.3076856
Sum22807859
Variance1.703943 × 109
MonotonicityNot monotonic
2023-03-10T14:29:37.553792image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
652 5
 
0.2%
663 5
 
0.2%
848 5
 
0.2%
828 5
 
0.2%
1899 4
 
0.2%
884 4
 
0.2%
1738 4
 
0.2%
1918 4
 
0.2%
1809 4
 
0.2%
1378 4
 
0.2%
Other values (2027) 2467
98.2%
ValueCountFrequency (%)
220 1
< 0.1%
231 2
0.1%
234 1
< 0.1%
236 1
< 0.1%
240 1
< 0.1%
267 1
< 0.1%
298 1
< 0.1%
305 1
< 0.1%
313 1
< 0.1%
330 1
< 0.1%
ValueCountFrequency (%)
418337 1
< 0.1%
414734 1
< 0.1%
411339 1
< 0.1%
408032 1
< 0.1%
404952 1
< 0.1%
401080 1
< 0.1%
396683 1
< 0.1%
392477 1
< 0.1%
388216 1
< 0.1%
384024 1
< 0.1%
Distinct2042
Distinct (%)81.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9092.9709
Minimum223
Maximum420326
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:38.025756image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum223
5-th percentile666.5
Q11378.5
median2316
Q34513
95-th percentile14227
Maximum420326
Range420103
Interquartile range (IQR)3134.5

Descriptive statistics

Standard deviation41326.538
Coefficient of variation (CV)4.5448884
Kurtosis70.375202
Mean9092.9709
Median Absolute Deviation (MAD)1342
Skewness8.3173252
Sum22832450
Variance1.7078828 × 109
MonotonicityNot monotonic
2023-03-10T14:29:38.496344image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1751 5
 
0.2%
1268 5
 
0.2%
1690 5
 
0.2%
656 4
 
0.2%
3248 4
 
0.2%
719 4
 
0.2%
817 4
 
0.2%
1488 4
 
0.2%
743 4
 
0.2%
820 4
 
0.2%
Other values (2032) 2468
98.3%
ValueCountFrequency (%)
223 1
< 0.1%
232 1
< 0.1%
233 1
< 0.1%
235 1
< 0.1%
240 1
< 0.1%
245 1
< 0.1%
286 1
< 0.1%
300 1
< 0.1%
303 1
< 0.1%
323 1
< 0.1%
ValueCountFrequency (%)
420326 1
< 0.1%
416347 1
< 0.1%
413120 1
< 0.1%
409557 1
< 0.1%
406506 1
< 0.1%
403397 1
< 0.1%
398762 1
< 0.1%
394604 1
< 0.1%
390349 1
< 0.1%
386082 1
< 0.1%
Missing0
Missing (%)0.0%
Memory size19.7 KiB
Distinct1914
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean312.1499
Minimum15.6
Maximum2217
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:38.893121image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum15.6
5-th percentile41.9
Q1113.9
median181.5
Q3319
95-th percentile1192.6
Maximum2217
Range2201.4
Interquartile range (IQR)205.1

Descriptive statistics

Standard deviation361.45127
Coefficient of variation (CV)1.1579413
Kurtosis7.4238556
Mean312.1499
Median Absolute Deviation (MAD)87.8
Skewness2.6063707
Sum783808.4
Variance130647.02
MonotonicityNot monotonic
2023-03-10T14:29:39.338561image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.9 5
 
0.2%
157.2 5
 
0.2%
60.3 5
 
0.2%
43.1 5
 
0.2%
128.8 5
 
0.2%
88.1 5
 
0.2%
93.6 5
 
0.2%
106.5 5
 
0.2%
40.8 5
 
0.2%
134.1 4
 
0.2%
Other values (1904) 2462
98.0%
ValueCountFrequency (%)
15.6 1
 
< 0.1%
15.9 1
 
< 0.1%
16.1 2
0.1%
16.2 2
0.1%
16.3 3
0.1%
16.4 1
 
< 0.1%
16.5 2
0.1%
16.6 1
 
< 0.1%
16.8 1
 
< 0.1%
16.9 2
0.1%
ValueCountFrequency (%)
2217 1
< 0.1%
2208.9 1
< 0.1%
2199.2 1
< 0.1%
2184.1 1
< 0.1%
2181.4 1
< 0.1%
2176.3 1
< 0.1%
2173.5 1
< 0.1%
2167.1 1
< 0.1%
2152.2 1
< 0.1%
2124.9 1
< 0.1%
Distinct204
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.034886
Minimum14.4
Maximum36.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:40.129613image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum14.4
5-th percentile19.6
Q123
median26.1
Q329.2
95-th percentile32.1
Maximum36.6
Range22.2
Interquartile range (IQR)6.2

Descriptive statistics

Standard deviation4.004619
Coefficient of variation (CV)0.15381742
Kurtosis-0.47049818
Mean26.034886
Median Absolute Deviation (MAD)3.1
Skewness-0.1764421
Sum65373.6
Variance16.036974
MonotonicityNot monotonic
2023-03-10T14:29:40.530028image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.8 31
 
1.2%
29.4 30
 
1.2%
29 29
 
1.2%
29.6 29
 
1.2%
24.9 29
 
1.2%
29.9 28
 
1.1%
29.5 28
 
1.1%
29.3 28
 
1.1%
22.7 27
 
1.1%
26 27
 
1.1%
Other values (194) 2225
88.6%
ValueCountFrequency (%)
14.4 1
 
< 0.1%
14.5 2
 
0.1%
14.8 1
 
< 0.1%
15 2
 
0.1%
15.3 1
 
< 0.1%
15.4 1
 
< 0.1%
15.6 2
 
0.1%
15.7 5
0.2%
15.8 4
0.2%
15.9 2
 
0.1%
ValueCountFrequency (%)
36.6 1
< 0.1%
36.2 1
< 0.1%
35.9 2
0.1%
35.8 2
0.1%
35.7 1
< 0.1%
35.5 2
0.1%
35.4 1
< 0.1%
35.3 2
0.1%
35.2 2
0.1%
35 1
< 0.1%
Distinct190
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.439745
Minimum46.9
Maximum71.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:40.962177image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum46.9
5-th percentile54.9
Q158.5
median60.6
Q362.5
95-th percentile65.3
Maximum71.3
Range24.4
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2212005
Coefficient of variation (CV)0.053296064
Kurtosis0.89178963
Mean60.439745
Median Absolute Deviation (MAD)2
Skewness-0.31137475
Sum151764.2
Variance10.376133
MonotonicityNot monotonic
2023-03-10T14:29:41.389055image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 44
 
1.8%
61.7 43
 
1.7%
61.8 41
 
1.6%
59.3 39
 
1.6%
60.3 38
 
1.5%
62.4 38
 
1.5%
62.2 37
 
1.5%
60.7 36
 
1.4%
61.6 36
 
1.4%
60.6 36
 
1.4%
Other values (180) 2123
84.5%
ValueCountFrequency (%)
46.9 1
< 0.1%
47.7 1
< 0.1%
47.8 1
< 0.1%
48 1
< 0.1%
48.2 1
< 0.1%
48.3 1
< 0.1%
48.4 1
< 0.1%
48.5 2
0.1%
48.7 1
< 0.1%
49.2 1
< 0.1%
ValueCountFrequency (%)
71.3 1
< 0.1%
71.1 1
< 0.1%
71 1
< 0.1%
69.7 1
< 0.1%
69.4 2
0.1%
69.3 2
0.1%
69.1 1
< 0.1%
69 2
0.1%
68.8 2
0.1%
68.7 1
< 0.1%
Distinct205
Distinct (%)8.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.524014
Minimum2.5
Maximum27.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:41.769801image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile8.1
Q111.3
median13.3
Q315.7
95-th percentile19.35
Maximum27.3
Range24.8
Interquartile range (IQR)4.4

Descriptive statistics

Standard deviation3.4973013
Coefficient of variation (CV)0.25859935
Kurtosis0.72781042
Mean13.524014
Median Absolute Deviation (MAD)2.2
Skewness0.25319253
Sum33958.8
Variance12.231116
MonotonicityNot monotonic
2023-03-10T14:29:42.182791image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12.4 46
 
1.8%
11.9 44
 
1.8%
12.6 40
 
1.6%
15.7 38
 
1.5%
11.6 37
 
1.5%
14.9 36
 
1.4%
13.6 35
 
1.4%
13.3 34
 
1.4%
14.4 33
 
1.3%
12.5 33
 
1.3%
Other values (195) 2135
85.0%
ValueCountFrequency (%)
2.5 1
 
< 0.1%
3 1
 
< 0.1%
3.3 3
0.1%
3.6 2
0.1%
3.8 1
 
< 0.1%
3.9 1
 
< 0.1%
4 1
 
< 0.1%
4.2 1
 
< 0.1%
4.3 2
0.1%
4.5 3
0.1%
ValueCountFrequency (%)
27.3 1
< 0.1%
27 1
< 0.1%
26.9 2
0.1%
26.8 1
< 0.1%
26.4 1
< 0.1%
26.2 2
0.1%
26 1
< 0.1%
25.6 1
< 0.1%
25.4 1
< 0.1%
25.1 1
< 0.1%

Jahr
Real number (ℝ)

Distinct31
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2006
Minimum1991
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.7 KiB
2023-03-10T14:29:42.548400image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Quantile statistics

Minimum1991
5-th percentile1992
Q11998
median2006
Q32014
95-th percentile2020
Maximum2021
Range30
Interquartile range (IQR)16

Descriptive statistics

Standard deviation8.9460535
Coefficient of variation (CV)0.0044596478
Kurtosis-1.2025046
Mean2006
Median Absolute Deviation (MAD)8
Skewness0
Sum5037066
Variance80.031873
MonotonicityIncreasing
2023-03-10T14:29:42.883664image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1991 81
 
3.2%
2007 81
 
3.2%
2020 81
 
3.2%
2019 81
 
3.2%
2018 81
 
3.2%
2017 81
 
3.2%
2016 81
 
3.2%
2015 81
 
3.2%
2014 81
 
3.2%
2013 81
 
3.2%
Other values (21) 1701
67.7%
ValueCountFrequency (%)
1991 81
3.2%
1992 81
3.2%
1993 81
3.2%
1994 81
3.2%
1995 81
3.2%
1996 81
3.2%
1997 81
3.2%
1998 81
3.2%
1999 81
3.2%
2000 81
3.2%
ValueCountFrequency (%)
2021 81
3.2%
2020 81
3.2%
2019 81
3.2%
2018 81
3.2%
2017 81
3.2%
2016 81
3.2%
2015 81
3.2%
2014 81
3.2%
2013 81
3.2%
2012 81
3.2%

Interactions

2023-03-10T14:29:31.460176image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:10.226374image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:12.994001image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:15.617427image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:18.585863image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:21.960739image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:24.763692image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:27.964796image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:31.831178image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:10.577718image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:13.335315image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:15.951874image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:18.996206image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:22.335643image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:25.140498image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:28.403126image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:32.190976image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:10.894199image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:13.652321image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:16.277052image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:19.712712image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:22.659899image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:25.569889image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:28.806285image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:32.537312image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:11.236752image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:13.960066image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:16.597032image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:20.091707image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:22.994710image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:25.927869image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:29.546859image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:32.903073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:11.619279image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:14.302665image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:17.245560image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:20.448003image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:23.335214image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:26.353502image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:29.948793image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:33.279815image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:11.962392image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:14.621017image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:17.567597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:20.851054image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:23.659888image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:26.746713image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:30.328073image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:33.672106image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:12.325775image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:14.952304image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:17.911493image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:21.209423image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:23.996171image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:27.125941image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:30.706772image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:34.027669image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:12.653316image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:15.286002image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:18.245500image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:21.559735image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:24.376868image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:27.588144image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
2023-03-10T14:29:31.081434image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/

Correlations

2023-03-10T14:29:43.213228image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Gemeinde IDMittlere WohnbevölkerungStändige Wohnbevölkerung TotalStändige Wohnbevölkerung Bevölkerungs-dichte1 in Pers./km2Ständige Wohnbevölkerung Anteil 0-19-Jährige in %Ständige Wohnbevölkerung Anteil 20-64-Jährige in %Ständige Wohnbevölkerung Anteil 65-Jährige und Ältere in %JahrGemeinde
Gemeinde ID1.000-0.077-0.0780.008-0.0140.016-0.0040.0000.986
Mittlere Wohnbevölkerung-0.0771.0001.0000.527-0.3470.1280.2790.1010.685
Ständige Wohnbevölkerung Total-0.0781.0001.0000.529-0.3490.1310.2770.1030.685
Ständige Wohnbevölkerung Bevölkerungs-dichte1 in Pers./km20.0080.5270.5291.000-0.3690.570-0.1230.1190.681
Ständige Wohnbevölkerung Anteil 0-19-Jährige in %-0.014-0.347-0.349-0.3691.000-0.559-0.604-0.7180.399
Ständige Wohnbevölkerung Anteil 20-64-Jährige in %0.0160.1280.1310.570-0.5591.000-0.2490.3480.462
Ständige Wohnbevölkerung Anteil 65-Jährige und Ältere in %-0.0040.2790.277-0.123-0.604-0.2491.0000.4790.482
Jahr0.0000.1010.1030.119-0.7180.3480.4791.0000.000
Gemeinde0.9860.6850.6850.6810.3990.4620.4820.0001.000

Missing values

2023-03-10T14:29:34.490234image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-03-10T14:29:35.155597image/svg+xmlMatplotlib v3.5.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GemeindeGemeinde IDMittlere WohnbevölkerungStändige Wohnbevölkerung TotalStändige Wohnbevölkerung Ausländer-anteil in %Ständige Wohnbevölkerung Bevölkerungs-dichte1 in Pers./km2Ständige Wohnbevölkerung Anteil 0-19-Jährige in %Ständige Wohnbevölkerung Anteil 20-64-Jährige in %Ständige Wohnbevölkerung Anteil 65-Jährige und Ältere in %Jahr
0Kanton LuzernNaN32847032627511.9218.525.560.913.51991
1Adligenswil1051.0433043566.6623.234.661.83.61991
2Aesch1021.08969126.6157.229.561.39.21991
3Alberswil1121.04324288.2121.126.264.09.81991
4Altbüron1122.07397494.1111.029.159.511.31991
5Altishofen1123.01541151510.6105.830.458.511.11991
6Ballwil1023.0188818914.5215.731.557.211.31991
7Beromünster1081.0507550538.6119.430.857.811.41991
8Buchrain1052.04091407915.6849.229.064.36.71991
9Büron1082.01686169020.4315.029.161.29.71991
GemeindeGemeinde IDMittlere WohnbevölkerungStändige Wohnbevölkerung TotalStändige Wohnbevölkerung Ausländer-anteil in %Ständige Wohnbevölkerung Bevölkerungs-dichte1 in Pers./km2Ständige Wohnbevölkerung Anteil 0-19-Jährige in %Ständige Wohnbevölkerung Anteil 20-64-Jährige in %Ständige Wohnbevölkerung Anteil 65-Jährige und Ältere in %Jahr
2501Udligenswil1067.02381239112.1384.121.758.619.72021
2502Ufhusen1145.09289286.776.021.961.316.82021
2503Vitznau1068.01429143127.9121.615.460.823.82021
2504Wauwil1146.02428247523.1834.921.162.816.02021
2505Weggis1069.04478451525.7178.614.458.726.92021
2506Werthenstein1009.02140215412.9136.323.660.715.72021
2507Wikon1147.01492148818.2179.722.458.519.12021
2508Willisau1151.08963901513.4190.922.058.219.82021
2509Wolhusen1107.04309430221.2301.023.259.817.02021
2510Zell1150.02097208614.2150.020.659.819.62021